A study of people in 15 countries reveals that while everyone favors rhythms with simple integer ratios, biases can vary quite a bit across societies.

“Minimum viewing time” benchmark gauges image recognition complexity for AI systems by measuring the time needed for accurate human identification.

Researchers coaxed a family of generative AI models to work together to solve multistep robot manipulation problems.

Images that humans perceive as completely unrelated can be classified as the same by computational models.

Using insights into how people intuit others’ emotions, researchers have designed a model that approximates this aspect of human social intelligence.

MIT researchers uncover the structural properties and dynamics of deep classifiers, offering novel explanations for optimization, generalization, and approximation in deep networks.

Neuroscience PhD student Fernanda De La Torre uses complex algorithms to investigate philosophical questions about perception and reality.

MIT scientists have discovered a population of neurons that light up whenever we see images of food.

A new computational model could explain differences in recognizing facial emotions.

Researchers find similarities between how some computer-vision systems process images and how humans see out of the corners of our eyes.